{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3218e0ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 1. Load and prepare data   ==============================================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from scipy import stats\n",
    "from statsmodels.formula.api import mixedlm\n",
    "\n",
    "dem_color = '#2171b5'\n",
    "rep_color = '#cb181d'\n",
    "\n",
    "df = pd.read_csv('Merged Perspective.csv', low_memory=False).drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "df[\"isRepost\"] = (\n",
    "    df[\"authorUsername\"].str.lower().ne(df[\"postedOnPage\"].str.lower()).astype(int)\n",
    ")\n",
    "df = df.loc[df[\"isRepost\"] == 0].drop(columns=[\"isRepost\"]).reset_index(drop=True)\n",
    "\n",
    "df[\"postedAt\"] = (\n",
    "    pd.to_datetime(df[\"postedAt\"], errors=\"coerce\", utc=True)\n",
    "    .dt.tz_convert(\"America/New_York\")\n",
    ")\n",
    "\n",
    "df[\"day_of_week\"] = df[\"postedAt\"].dt.dayofweek\n",
    "df[\"is_weekend\"] = df[\"day_of_week\"].isin([5, 6]).astype(int)\n",
    "df[\"hour\"] = df[\"postedAt\"].dt.hour\n",
    "\n",
    "df[\"time_of_day\"] = pd.cut(\n",
    "    df[\"hour\"],\n",
    "    bins=[-1, 6, 11, 16, 21, 24],\n",
    "    labels=[\"night\", \"morning\", \"afternoon\", \"evening\", \"night\"],\n",
    "    ordered=False,\n",
    ").astype(str)\n",
    "\n",
    "df[\"tweet_length\"] = df[\"tweetText\"].str.len().fillna(0).astype(int)\n",
    "df[\"has_mention\"] = df[\"tweetText\"].str.contains(\"@\", na=False).astype(int)\n",
    "df[\"first_mention\"] = df[\"tweetText\"].str.startswith(\"@\", na=False).astype(int)\n",
    "df[\"has_hashtag\"] = df[\"tweetText\"].str.contains(\"#\", na=False).astype(int)\n",
    "\n",
    "target_cols = ['likes', 'views', 'reposts', 'quotes']\n",
    "df[target_cols] = df[target_cols].replace(0, 0.1)\n",
    "\n",
    "for var in target_cols:\n",
    "    df[f'log_{var}'] = np.log(df[var])\n",
    "\n",
    "nlp_cols = [\n",
    "    \"TOXICITY\", \"SEVERE_TOXICITY\", \"IDENTITY_ATTACK\", \"INSULT\", \"PROFANITY\",\n",
    "    \"THREAT\", \"SEXUALLY_EXPLICIT\", \"AFFINITY_EXPERIMENTAL\",\n",
    "    \"COMPASSION_EXPERIMENTAL\", \"CURIOSITY_EXPERIMENTAL\", \"NUANCE_EXPERIMENTAL\",\n",
    "    \"PERSONAL_STORY_EXPERIMENTAL\", \"REASONING_EXPERIMENTAL\", \"RESPECT_EXPERIMENTAL\"\n",
    "]\n",
    "\n",
    "negative_attrs = [\n",
    "    \"TOXICITY\", \"SEVERE_TOXICITY\", \"IDENTITY_ATTACK\", \"INSULT\", \"PROFANITY\",\n",
    "    \"THREAT\", \"SEXUALLY_EXPLICIT\"\n",
    "]\n",
    "positive_attrs = [\n",
    "    \"AFFINITY_EXPERIMENTAL\", \"COMPASSION_EXPERIMENTAL\", \"CURIOSITY_EXPERIMENTAL\",\n",
    "    \"NUANCE_EXPERIMENTAL\", \"PERSONAL_STORY_EXPERIMENTAL\",\n",
    "    \"REASONING_EXPERIMENTAL\", \"RESPECT_EXPERIMENTAL\"\n",
    "]\n",
    "\n",
    "for cols, threshold in [(negative_attrs, 0.3), (positive_attrs, 0.7)]:\n",
    "    for col in cols:\n",
    "        df[f\"threshold_{col}\"] = (df[col] >= threshold).astype(int)\n",
    "\n",
    "threshold_cols = [col for col in df.columns if col.startswith(\"threshold_\")]\n",
    "start_date = df[\"postedAt\"].min()\n",
    "end_date = df[\"postedAt\"].max()\n",
    "num_rows = len(df)\n",
    "unique_authors = df[\"authorUsername\"].nunique()\n",
    "\n",
    "parties = {\n",
    "    \"All Members\": df,\n",
    "    \"Democrats\": df[df[\"Party\"] == \"D\"],\n",
    "    \"Republicans\": df[df[\"Party\"] == \"R\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4083b72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in the dataframe: 183476\n",
      "Timeframe for analysis: 2022-12-31 22:00:22-05:00 to 2024-04-10 23:51:10-04:00\n",
      "Number of unique authors: 523\n",
      "================================================================================\n",
      "TABLE 1: ATTRIBUTE SUMMARY BY PARTY\n",
      "================================================================================\n",
      "                            All Members                                    \\\n",
      "                               # Tweets # Flagged Pct. Flagged Mean Score   \n",
      "TOXICITY                         183476      5366         2.92      0.056   \n",
      "SEVERE_TOXICITY                  183476         8         0.00      0.002   \n",
      "IDENTITY_ATTACK                  183476      2172         1.18      0.023   \n",
      "INSULT                           183476      2850         1.55      0.029   \n",
      "PROFANITY                        183476       278         0.15      0.017   \n",
      "THREAT                           183476       898         0.49      0.014   \n",
      "SEXUALLY_EXPLICIT                183476       663         0.36      0.010   \n",
      "AFFINITY_EXPERIMENTAL            183476    110782        60.38      0.683   \n",
      "COMPASSION_EXPERIMENTAL          183476     88484        48.23      0.627   \n",
      "CURIOSITY_EXPERIMENTAL           183476      2779         1.51      0.222   \n",
      "NUANCE_EXPERIMENTAL              183476      2212         1.21      0.342   \n",
      "PERSONAL_STORY_EXPERIMENTAL      183476     42100        22.95      0.498   \n",
      "REASONING_EXPERIMENTAL           183476     34578        18.85      0.513   \n",
      "RESPECT_EXPERIMENTAL             183476    131018        71.41      0.749   \n",
      "\n",
      "                            Democrats                                    \\\n",
      "                             # Tweets # Flagged Pct. Flagged Mean Score   \n",
      "TOXICITY                        97610      1919         1.97      0.047   \n",
      "SEVERE_TOXICITY                 97610         0         0.00      0.002   \n",
      "IDENTITY_ATTACK                 97610       916         0.94      0.020   \n",
      "INSULT                          97610      1005         1.03      0.023   \n",
      "PROFANITY                       97610       142         0.15      0.016   \n",
      "THREAT                          97610       296         0.30      0.013   \n",
      "SEXUALLY_EXPLICIT               97610       258         0.26      0.009   \n",
      "AFFINITY_EXPERIMENTAL           97610     66183        67.80      0.725   \n",
      "COMPASSION_EXPERIMENTAL         97610     56855        58.25      0.679   \n",
      "CURIOSITY_EXPERIMENTAL          97610       812         0.83      0.213   \n",
      "NUANCE_EXPERIMENTAL             97610      1384         1.42      0.366   \n",
      "PERSONAL_STORY_EXPERIMENTAL     97610     24759        25.37      0.526   \n",
      "REASONING_EXPERIMENTAL          97610     22331        22.88      0.555   \n",
      "RESPECT_EXPERIMENTAL            97610     78416        80.34      0.793   \n",
      "\n",
      "                            Republicans                                    \n",
      "                               # Tweets # Flagged Pct. Flagged Mean Score  \n",
      "TOXICITY                          85027      3424         4.03      0.066  \n",
      "SEVERE_TOXICITY                   85027         8         0.01      0.002  \n",
      "IDENTITY_ATTACK                   85027      1255         1.48      0.027  \n",
      "INSULT                            85027      1832         2.15      0.035  \n",
      "PROFANITY                         85027       134         0.16      0.018  \n",
      "THREAT                            85027       595         0.70      0.016  \n",
      "SEXUALLY_EXPLICIT                 85027       404         0.48      0.010  \n",
      "AFFINITY_EXPERIMENTAL             85027     44182        51.96      0.635  \n",
      "COMPASSION_EXPERIMENTAL           85027     31140        36.62      0.567  \n",
      "CURIOSITY_EXPERIMENTAL            85027      1949         2.29      0.232  \n",
      "NUANCE_EXPERIMENTAL               85027       810         0.95      0.315  \n",
      "PERSONAL_STORY_EXPERIMENTAL       85027     17248        20.29      0.466  \n",
      "REASONING_EXPERIMENTAL            85027     12077        14.20      0.463  \n",
      "RESPECT_EXPERIMENTAL              85027     52061        61.23      0.698  \n",
      "\n",
      "================================================================================\n",
      "TABLE 2: MEAN ENGAGEMENT BY PARTISANSHIP\n",
      "================================================================================\n",
      "             likes    views  reposts  quotes\n",
      "All Members  427.0  27593.6    102.9   112.5\n",
      "Democrats    349.7  22125.5     87.5    97.2\n",
      "Republicans  473.7  29916.1    116.0   120.4\n",
      "\n",
      "================================================================================\n",
      "TABLE 3: MEAN ENGAGEMENT FOR FLAGGED POSTS BY ATTRIBUTE\n",
      "================================================================================\n",
      "                              likes     views  reposts  quotes\n",
      "TOXICITY                     1518.5   85895.9    354.7   414.5\n",
      "SEVERE_TOXICITY              2947.4   72900.0    347.8   674.0\n",
      "IDENTITY_ATTACK              1044.6   83961.2    257.2   285.8\n",
      "INSULT                       1675.5   83397.3    393.4   456.7\n",
      "PROFANITY                    2946.1  189188.0    616.9   655.9\n",
      "THREAT                       1879.3  126650.8    420.0   526.4\n",
      "SEXUALLY_EXPLICIT            1577.7   89700.3    352.2   446.7\n",
      "AFFINITY_EXPERIMENTAL         256.3   17482.7     64.5    57.0\n",
      "COMPASSION_EXPERIMENTAL       257.5   19241.2     63.5    62.8\n",
      "CURIOSITY_EXPERIMENTAL       1347.6   80160.2    313.0   373.1\n",
      "NUANCE_EXPERIMENTAL           277.0   16574.0     46.1    75.9\n",
      "PERSONAL_STORY_EXPERIMENTAL   218.9   15552.3     46.7    43.4\n",
      "REASONING_EXPERIMENTAL        116.8    9436.1     30.4    28.1\n",
      "RESPECT_EXPERIMENTAL          208.1   15498.1     53.6    44.4\n"
     ]
    }
   ],
   "source": [
    "## 2. Descriptive Stats   ==============================================\n",
    "\n",
    "print(f\"Number of rows in the dataframe: {num_rows}\")\n",
    "print(f\"Timeframe for analysis: {start_date} to {end_date}\")\n",
    "print(f\"Number of unique authors: {unique_authors}\")\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "#   TABLE 1: ATTRIBUTE-LEVEL SUMMARY (rows = attributes)\n",
    "# ------------------------------------------------------------------\n",
    "print(\"=\" * 80)\n",
    "print(\"TABLE 1: ATTRIBUTE SUMMARY BY PARTY\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "attr_rows = []\n",
    "for attr in nlp_cols:\n",
    "    row = []\n",
    "    for _, party_df in parties.items():\n",
    "        total = len(party_df)\n",
    "        flagged = party_df[f\"threshold_{attr}\"].sum()\n",
    "        mean_val = party_df[attr].mean()\n",
    "        row.extend([\n",
    "            total,\n",
    "            int(flagged),\n",
    "            round((flagged / total) * 100, 2) if total else np.nan,\n",
    "            round(mean_val, 3)\n",
    "        ])\n",
    "    attr_rows.append(row)\n",
    "\n",
    "attr_cols = pd.MultiIndex.from_product(\n",
    "    [parties.keys(), [\"# Tweets\", \"# Flagged\", \"Pct. Flagged\", \"Mean Score\"]]\n",
    ")\n",
    "attr_table = pd.DataFrame(attr_rows, index=nlp_cols, columns=attr_cols)\n",
    "print(attr_table)\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 3.  TABLE 2: MEAN ENGAGEMENT BY PARTISANSHIP\n",
    "# ------------------------------------------------------------------\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"TABLE 2: MEAN ENGAGEMENT BY PARTISANSHIP\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "eng_by_party_table = pd.DataFrame(\n",
    "    [[round(party_df[col].mean(), 1) for col in target_cols] for party_df in parties.values()],\n",
    "    index=list(parties.keys()),\n",
    "    columns=target_cols\n",
    ")\n",
    "print(eng_by_party_table)\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 4.  TABLE 3: MEAN ENGAGEMENT FOR POSTS FLAGGED BY EACH ATTRIBUTE\n",
    "# ------------------------------------------------------------------\n",
    "print(\"\\n\" + \"=\" * 80)\n",
    "print(\"TABLE 3: MEAN ENGAGEMENT FOR FLAGGED POSTS BY ATTRIBUTE\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "flagged_eng_table = pd.DataFrame(\n",
    "    [\n",
    "        [round(df.loc[df[f\"threshold_{attr}\"] == 1, col].mean(), 1) for col in target_cols]\n",
    "        if df[f\"threshold_{attr}\"].sum() > 0\n",
    "        else [np.nan] * len(target_cols)\n",
    "        for attr in nlp_cols\n",
    "    ],\n",
    "    index=nlp_cols,\n",
    "    columns=target_cols\n",
    ")\n",
    "print(flagged_eng_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b645efb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nr/kvb4nyt11013kvjmk27331w00000gn/T/ipykernel_36573/2842821251.py:125: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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Sl2mTJk3s8p49e7LVUE3F2eXff//N8nMg+3QweO2112Y4tUbTvEQNFTIK5q9YscI8/vjjdoqrvxFE9erVTZcuXew018x0sNe+NnjwYDtVrESJEvb6t99+Owtbi2Tdt/777z97AKl9Rw1AMqLXcfugmkciMelzxL3P69evD3k/3ebux2dPctqxY4fXNEhNZrNLxz4vvfSSueCCC+xnk2tYqynMmi7tgizBVK5c2e6LLti8cOFC+1mrslma9qqfarq0atWqbK8nokfvo6anuwCeps1nZ//VFHt3HKX9RPvRjTfeGLI5l76b3efezJkzM3yNW2+91d5X38t//vlnltcVOSvezys1gLZ37157zKem34hdsXQe4HCOifQQ6EWuOnjwoLcc7MPv999/92oyZfSF527/3//+l+5JbXbWJxy//vqr+emnn7yDCESXDvRlyZIl5ueff05z+y+//GJravnvG4o6pNapU8eOtqteofa1Q4cO2S7la9eutV/mjRo1Mn379g1r3VS768wzz7TZLNpn9IWM+BEr+5bqZapuuOiEVY9Nz1tvvWV/5suXz3scgOTlzxpbuXJltp5LvQl07HPXXXeZOXPm2FqWGoxSMG727NmmR48epnHjxvb6jOizSvcdN26crXGpYzT91ImqvjtVCxPxS99nLpvSZTFmlr4vNTjxxBNPeMdR2k82bNhg3nvvPdOsWTNz5513psmmbNu2rffa77//frqvof3XZYe2atXKlCxZMkvripwXz+eVH374oR30KFWqlHn22Wez9HpIzvOAYDjHRCACvchVKlzvnHbaaUE/IJ2Mgqb+27NyoqIDORX0l/Lly9sv2nDt37/ffqCqGYAODFy2yj333JPp9UBkaRTz9NNPDzni6q6rXbu2/UJMj97XIkWK2KljmlKlL2V9gU+bNs1OF6tUqZK9n6ZzjR49OsN1a9++vf0i1wmxAnSLFi2yU3xcFgFiWyztWwqeiE5mXfZAqM85nfzKZZddZrPtACQ3He+4zxgFaocMGRLWNONAOhnVMZCCbMWLF7cnpBMnTrTfbdOnT7fZahpgUpbuVVddZT+PQtGJac+ePW0msKZRf//99/aY8aGHHrJZTgqgXH/99fa5EZ80rf22226zywpshMq+TW8fad26tfn777/tgKem03/55Zc2O/j11183p5xyir3fyy+/nCY4opl3ajjoptWnNwNv6tSpZvfu3XZZ+xxiV7yeV/7111+2wZzo81dlKBD7Yuk8IBDnmEgj2kWCkTyOHDmS0qBBA68o+aJFi9Lc59VXX/Vu/+ijj9J9Pt3u7vvaa69len3UWMQ9/s4778zw/mpW4u4f7NKnT5+Uo0ePZno9ELlC+a6hzJAhQ+zvFSpUSPWeaFnX6bZnnnkmzfsaWChfjR7+/PPPkK998ODBlIsuusg+tlKlSkEbRvgbG+XNmzdl+vTpEdx6JPO+1axZM3u7mpGE8sknn3jroMaRSNxmbP797ffffw/5HLotvSZcNGNLDs8++2yqY5jKlSun3H333Snjxo1L+e2338J6jsaNG9vHnnXWWfYzLZipU6fa7z7db+TIkWlu1+ebWwctB2tyM2fOHK+5Tf369bOwtYiV71A1KipSpIi9rkWLFplqxqb33jVDCnYstXv37pRatWp5x1srVqxIdfvnn38e1jmGa6BUvHjxlAMHDmRx65HT4vm88uabb7b3a9KkSapjSf/3L827oi/WzwM4x0R6yOhFrlFxe1eTq127duacc85Jc59//vnHW1aNmvRoFMxRfaPM+O2330z//v291wl3WkQwGrHTdqkIv6uRg+hSBkbevHnNpk2b7Aipo2Vdp9tUOykjGmFXjaP0pr8OHTrULiujyZXwCEU1CFXMH/ErlvYtl9W7evVqs2DBgqDP47IAlCV3xRVXhLGFAJKBsiG7devm/a6pyi+88IK55pprTJUqVWz2v5YnTZoUtF6lms9+8803dvmdd94JmZGmOuLKNJKMagUqiynYrAPV4bz55pvtsrKDyeqNX/ouUmkFUTauLuHQcbbee9G+EOxYSiUWRo4caZeVof7KK6+k2RdLly5tl8eOHRv0dXQ+8fnnn9vlq6++2tb/RWyK1/NKfXa+8cYbdraDMjk5d4wvsXQe4Mc5JgIR6EWuTa3p06ePd5D36quvBr2ffypVRp1HXfdecU1Fwi27oAMCV7tGUwRPPPHEDB/Xpk0bs3z5cnvRgYWmQ6jmlz54VZxddZYQG0466SSvQYN/ao1bbtmypb1PZmnqqDrkaiqYCujr4j8B1hTY9DAFMP7F0r7VoUMHc9xxx9nlYNO6VBNTU1BdrTCdVACA6GRUjdI0hV4BsMDPB31+jB8/3k53b9CgQZpGRy4YpmmhqjOYnvPOO8/+VKAuVGM2BelU3iEUf1B61qxZYWwhYtUDDzzgNTEOt1av/z3v3r17yPupGZabwh+4n6jcg743Rd+Nmj4fSKVH3DkFx2yxK17PK1WDVU0JdXynwTZN8Ud8iaXzAD8+rxCIQC9ynIqVKyCqg3uNjKuZhr6Ug/GPnOvLMNyC967BQka0DjrIcx+WqhXmOj1nRKNu+kLWpX79+jbTRXW+VB9TI7k6QaGzZexwTac+/vhje8Cmi2uukZmGVCqKr2ztunXr2tF+1UxSfSad2Oqiek3Ozp07032uM844I8vbg9gRK/uWPvdc1oCaeuhkw08HnS6o4g+SAIBz0UUX2aCXOsRPmTLFPProo7YOqhtEEmXQqsnVli1bUl3nGtK6rt+hLi6DUzUsXe3TQPq8S28wSrOnXKBGA+6IX8qqdT0tNBtF9ZwzoqCHaB/IqPZlw4YN7U/10gg8l3DBEJ1DuO9tP9eoTYE6F8xBbInn88onn3zSrFq1ylSsWNEMHDgwrNdA7ImV8wA/zjERiEAvcpS6nWoawZ9//mm7j6qTssvsCMaN8IczbUYfjuFOxxGNiunLVycyouLnL730kskuZcrpS17TxHQyE+okBrlLo+uFCxe2TTs+++wz8+mnn9opXPoi1W3h0FRWfdH269fPLFu2zBw5ciTd+2eUAUDn5sQQS/uWK9+g1w88aXVZvjrprVWrVphbByAZqZmaGjaqY7eydZXR+9Zbb3nfWwry+rMvt2/fnqXXCRyQckIFahwFgV1zI46z4t+9997rTVsOJ+Dl3nPtAxnNTnHlP3Tcr/OPwIxf1+QosHyD9mmXBaxkDmW9I7bE83mlArwK6rmsX3+pCMSXWDoPcDjHRCC+wZBjNm/ebC688EL7UxkdOmFIb1qenHzyyd7yH3/8ke59VQfHqVChQobro+7P7qBOJzPqRB+pgzi3XTpIULdMRJ8O0jTi7zIb3ZQaXRfuwZWC+Dqo1P6rjEhNcdV+p6lgCuzrIM//xRysjqGfDkoR/2Jp3zr77LO9EX9/+QZ1rHfdpsnmBZBZmsbctWtXW6bK0SwmfT6J+3xSJpIraxXOJdSUVupUJhcFeRXsdd9X4ZY/y+5+ose7mTDz5s2zXeodzYxxs2CYBh174v28UjWFlVWsGuga8FKQOvDiMtdlzpw53vX+IDSiL5bOAxzOMRGIgn3IEZpeoOmAKmngRi7DmcrgzzrTyGd6/Le7elyhPPTQQ179Jo38aqqFanVFStmyZb1lFUxHbNA+p4MwfXn6rwuH9q/58+fbZY22Pv7440HvR2ZRcoqlfUtZvTrhUM06HTSecsopXtBXGQfKTELi859gumBcMJwwIjMuueQSG/TQCaiy6FTiQcc8rqmVsuQiUWdSGcTpUQDOn9WJ+KfyDSNGjLD7lLJ602sY6t5z3Vf7QnpZvVu3brU/FUAJluWmIK4yK/U5qYGM+++/P1XZhpo1a9pBVMSORDivdKUhtA3q7ZKRwYMHe8s6tiMDOLbE0nkAEAwZvYg4FaPXiYHLJnv66adtECIcClC4AvYKWqRHI/Gi7JDKlSuHvJ8+PJ955hm7rNq6yhoIt/ZSuPwZAeFM90HuuOCCC8wJJ5xgTwp00b6l68KtAeZ06tQp5P3o/p2cYmnf0kmrPtM02q864ZrepQwQ1zVcU7KR+PxTVAOnK/utXr06l9YIicLfWMhlVLqZBApauMBadqixbahGbaIamK7GJg2MEuczS43ZZPHixbYRWijuPdc+kFH3eTVMlurVqwdtwKUamMpE9wd3FUj79ttv7TLZvLElGc8rEfti6TwACIZALyJKU1Euv/xye8Am/fv3t6Oe4dIJhJuGo9Gu7777Luj9dL0bedX9Q03lUqaAqymnOjgqq+A/GY4UNQJwMuo+jdyjaSyaGqMpqLpoOdxyHf4TzvQy4F577bWIrCviSyztW2qa1L59e7v8zjvv2Fq9rvszZRuSh05owzk58E/FB8I5rnMBFg0auUzeK6+80v7UAJOOtbJLmUuTJk0KebumaTuavo3EoN4Wrj6zsnpDTU/2v+f+fSGQgrVuf01vP3HB3CVLlpiVK1d6AV9xpR0QfYl0XqmBeO3f6V389aq//PJL7/r0As+Ijlg6DwCCIdCLiNEou2rTqIOu9OrVK+RUhIymcrk6M3fddVea4uP6XdeLpm65zr2BNHW5d+/edrlGjRpm5syZmZ7upy9l1cpJj2ouuUL8OtFWZ2rEjiFDhtj3UBdlAYRLmSD+/SAYTdtSEX4kp1jat1xTNpWOefDBB+1y1apVTfPmzcN+DsQ3Zby57zg1hPF3EPfXoPQPTCI5qdyCmjQqEy29Mh+6TcdbajLjgrsuAKKGSA0aNLDLQ4cOtftWelSfN71Arqhma7ASDsrEGzlypF0+55xzbBYdEoOmo7vAnfYRdzwdSPtavXr17PKoUaPM7Nmz09xHA5y33nqrXVbA5bbbbgv5upo67/ZlTb92A2CNGjWyNVQRfYl4XonEEkvnAUAgavQiYnTQ5OrUtGzZ0nTv3j1VUflAmk6lL8pAuk5TufSBqawkdcjVQaCCFuvWrbMfqhqBF93P/2HpqPvlzTffbEdBlYGiEdgdO3bYSygK0gbWPxo0aJC577777PTnpk2b2nVQaQad9OiAVAeH7gBE26MTEYqhJwZNS1XgRPvw66+/bqdCa7RW03TU0EFNF5Q5qf3T7QNAtPYt1YjTZ6em5btp1OoGTYOj5KETVAU5VHtS+5a+hxX0r1ixog2eKcCrE4rGjRubb775JtqriyjT9PbWrVvbacpt2rSxAa5KlSrZ7LS//vrLHmcpc1LHOm7mgL9mpCgLUgE4ZeNq+qk+u/RTx2U6Ftq+fbt9HgV4lTGn4ym9ZjCaSq9MTAVy+/bta59XgxUK/GlA3dVlffnll3Pl74Pco4Dss88+a7Zs2WJrsYaiAK8GKBQAbNWqlQ3OaX/Ssbv2M503uBquqrubXokPNenSQOjcuXPtPqV9XijbEDsS8bwSEM4xkStSgAjR7pSZS6VKlUI+15EjR1K6deuW7uO7d+9u7xdMly5dMr0+X375ZZrn0TqG89iTTz45ZcaMGRH9eyI8et/c+zB69OhMP16PCbUPLFmyJKVkyZIh3/c6deqkbN682ft94MCBaZ5f17nbEV9ifd8KNGTIEO/+efPmTdm0aVOm1xnxs08OGjQoze379u1LOffcc0PuV+eff37KihUr0t2v+cxKfAcOHEg5/vjjwz4+ql69esqiRYuCPtevv/6aUrt27bCe59FHHw15nKXjtlGjRqXky5cv6GMLFCiQ8sEHH+TCXwfR+A598cUX07znwUyfPj2lePHi6e5nd9xxR8jzAz/tb/7Had/bvn17lrcXkZWI55UZ8X//ZuXxSK7zAI7XkB5KNyAmacrVm2++aSZPnmxrJanAuUZq9VO/K8PjjTfeCLsWTlZNnz7dDBs2zLRr186cccYZpnz58jajRBkvGglWpq+m8vz666+2GywSy5lnnmmbfvTs2dNmOqmjrqZpKdNI2SfKiNLoKxAL+5ayARx9HiljCYnFNaOSwoULp7ld182ZM8c88cQTtn6gGsQo+0hT3VXOYdasWWQYwRQqVMg2kVWm0KOPPmouu+wyO11d+4YycbXP1KxZ02bnKmtXWUfKtA1G2XL6LNP9dEykDHLtdzpm02fY+eefbwYMGGB+/PFH88gjj2RYgubrr782HTt29I77lHGsTubKuLvmmmty6C+CaFO2ZIUKFTK8n0qGrF271naq1/eo9lXVx9R+p2xc7T/6rAvn/EC17fVY/3OXLVs229uC2BMr55WAwzkmcloeRXtz/FUAAECOUr04najK+PHjbbAEiUXvqwt2aRqzq80MxCs1GVJt8S5duoSsVQgAAIDwMWwFAEACcJ3IS5cu7XWZRmL5+eefvWVlXAIAAACAH4FeAADinBqKqHGDdO3aNdV0VCQGNaZyneE1Xdl1oAcAAAAAJ5+3BAAA4oZqbO7fv992GVcHaXWlV+3N3r17R3vVECG7du2yHZjXr19va7apNqWoZIPeawAAAADwI9ALAEAcUuOZr776KtV1gwcPts1FkBjefffdNIH7c8891zbQAgAAAIBABHoBAIhjhQsXtp3v77nnHtvQCIklT5485rjjjjOnnXaabbB32223UZoDAAAAQFB5UlJSUoLfBAAAAAAAAACIBzRjAwAAAAAAAIA4R6AXAAAAAAAAAOIcgV4AAAAAAAAAiHMEegEAAAAAAAAgzhHoBQAAAAAAAIA4R6AXAAAAAAAAAOIcgV4AAAAAAAAAiHMEegEAAAAAAADAxLf/B+6I6thHZXDIAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 3. Figures   ==============================================\n",
    "# ------------------------------------------------------------------\n",
    "#   FIGURE 1: Descriptives of Congressional Tweet Activity January 2023-April 2024\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "party_label_map = {'D': 'Democrats', 'R': 'Republicans', 'I': 'Independents'}\n",
    "party_color_map = {'D': dem_color, 'R': rep_color}\n",
    "fallback_color = '#6c757d'\n",
    "\n",
    "total_tweets = len(df)\n",
    "if total_tweets == 0:\n",
    "    raise ValueError(\"Dataset is empty; cannot plot summary charts.\")\n",
    "\n",
    "party_counts = df['Party'].dropna().value_counts()\n",
    "party_percents = (party_counts / total_tweets * 100).sort_values(ascending=False)\n",
    "party_codes = party_percents.index.tolist()\n",
    "party_labels = [party_label_map.get(code, code) for code in party_codes]\n",
    "party_colors = [party_color_map.get(code, fallback_color) for code in party_codes]\n",
    "\n",
    "def dominant_party(series):\n",
    "    non_null = series.dropna()\n",
    "    return non_null.mode().iloc[0] if not non_null.empty else np.nan\n",
    "\n",
    "poster_counts = df['authorUsername'].value_counts().head(5)\n",
    "poster_df = (\n",
    "    poster_counts.to_frame('tweet_count')\n",
    "    .join(df.groupby('authorUsername')['Party'].apply(dominant_party).rename('Party'), how='left')\n",
    "    .reset_index()\n",
    "    .rename(columns={'index': 'authorUsername'})\n",
    ")\n",
    "poster_df['party_label'] = poster_df['Party'].map(party_label_map).fillna('Unknown')\n",
    "poster_df['color'] = poster_df['Party'].map(party_color_map).fillna(fallback_color)\n",
    "poster_df = poster_df.sort_values('tweet_count', ascending=True)\n",
    "\n",
    "if df['postedAt'].notna().any():\n",
    "    weekly_counts = (\n",
    "        df[['postedAt']]\n",
    "        .dropna()\n",
    "        .set_index('postedAt')\n",
    "        .resample('W-SUN')\n",
    "        .size()\n",
    "        .reset_index(name='tweet_count')\n",
    "        .sort_values('postedAt')\n",
    "    )\n",
    "    weekly_counts = weekly_counts.iloc[:-3] if len(weekly_counts) > 3 else pd.DataFrame(columns=['postedAt', 'tweet_count'])\n",
    "    if not weekly_counts.empty:\n",
    "        weekly_counts['postedAt'] = weekly_counts['postedAt'].dt.tz_convert(None)\n",
    "else:\n",
    "    weekly_counts = pd.DataFrame(columns=['postedAt', 'tweet_count'])\n",
    "\n",
    "fig = plt.figure(figsize=(16, 12))\n",
    "gs = fig.add_gridspec(2, 2, height_ratios=[1, 1.2], hspace=0.45, wspace=0.4)\n",
    "\n",
    "ax_party = fig.add_subplot(gs[0, 0])\n",
    "bars = ax_party.bar(\n",
    "    party_labels, party_percents.values, color=party_colors,\n",
    "    edgecolor='black', linewidth=0.8, alpha=0.7\n",
    ")\n",
    "ax_party.set_ylim(0, 100)\n",
    "ax_party.set_ylabel('Percent of tweets (%)', fontsize=20)\n",
    "ax_party.set_title('Share of tweets by party', fontsize=20, pad=18)\n",
    "ax_party.tick_params(axis='x', labelsize=16)\n",
    "ax_party.tick_params(axis='y', labelsize=16)\n",
    "ax_party.grid(linestyle='--', alpha=0.3)\n",
    "ax_party.spines['top'].set_visible(False)\n",
    "ax_party.spines['right'].set_visible(False)\n",
    "for bar, pct in zip(bars, party_percents.values):\n",
    "    ax_party.text(\n",
    "        bar.get_x() + bar.get_width() / 2,\n",
    "        bar.get_height() + 2,\n",
    "        f'{pct:.1f}%',\n",
    "        ha='center',\n",
    "        va='bottom',\n",
    "        fontsize=14\n",
    "    )\n",
    "\n",
    "ax_posters = fig.add_subplot(gs[0, 1])\n",
    "poster_bars = ax_posters.barh(\n",
    "    poster_df['authorUsername'], poster_df['tweet_count'],\n",
    "    color=poster_df['color'], edgecolor='black', linewidth=0.8, alpha=0.7\n",
    ")\n",
    "ax_posters.set_xlim(0, 1000)\n",
    "ax_posters.set_xlabel('Number of tweets', fontsize=16)\n",
    "ax_posters.set_title('Top 5 posters', fontsize=20, pad=18)\n",
    "ax_posters.tick_params(axis='x', labelsize=16)\n",
    "ax_posters.tick_params(axis='y', labelsize=16, pad=24)\n",
    "ax_posters.grid(False)\n",
    "ax_posters.spines['top'].set_visible(False)\n",
    "ax_posters.spines['right'].set_visible(False)\n",
    "for bar in poster_bars:\n",
    "    count = bar.get_width()\n",
    "    ax_posters.text(\n",
    "        min(980, count + 15),\n",
    "        bar.get_y() + bar.get_height() / 2,\n",
    "        f'{int(count)}',\n",
    "        va='center',\n",
    "        fontsize=14\n",
    "    )\n",
    "\n",
    "ax_timeline = fig.add_subplot(gs[1, :])\n",
    "if weekly_counts.empty:\n",
    "    ax_timeline.text(0.5, 0.5, 'No dated tweets available after trimming', ha='center', va='center', fontsize=18)\n",
    "    ax_timeline.axis('off')\n",
    "else:\n",
    "    ax_timeline.plot(\n",
    "        weekly_counts['postedAt'],\n",
    "        weekly_counts['tweet_count'],\n",
    "        color=\"#151515\",\n",
    "        linewidth=2,\n",
    "        markersize=6,\n",
    "        markerfacecolor='white',\n",
    "        markeredgecolor=\"#151515\"\n",
    "    )\n",
    "    ax_timeline.set_ylabel('Posts per week', fontsize=20)\n",
    "    ax_timeline.set_title('Weekly post volume', fontsize=20, pad=18)\n",
    "    ax_timeline.grid(axis='y', linestyle='--', alpha=0.3)\n",
    "    ax_timeline.spines['top'].set_visible(False)\n",
    "    ax_timeline.spines['right'].set_visible(False)\n",
    "    ax_timeline.tick_params(axis='both', labelsize=20)\n",
    "    locator = mdates.AutoDateLocator()\n",
    "    ax_timeline.xaxis.set_major_locator(locator)\n",
    "    ax_timeline.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator))\n",
    "    plt.setp(ax_timeline.get_xticklabels(), rotation=0, ha='center')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe07c103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------\n",
    "#   FIGURE 2: Partisan Differences in Perspective Attributes of Congressional Tweets\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "attr_map = {\n",
    "    'threshold_TOXICITY': 'Toxicity',\n",
    "    'threshold_SEVERE_TOXICITY': 'Severe toxicity',\n",
    "    'threshold_IDENTITY_ATTACK': 'Identity attack',\n",
    "    'threshold_INSULT': 'Insult',\n",
    "    'threshold_PROFANITY': 'Profanity',\n",
    "    'threshold_THREAT': 'Threat',\n",
    "    'threshold_SEXUALLY_EXPLICIT': 'Sexually explicit',\n",
    "    'threshold_AFFINITY_EXPERIMENTAL': 'Affinity',\n",
    "    'threshold_COMPASSION_EXPERIMENTAL': 'Compassion',\n",
    "    'threshold_CURIOSITY_EXPERIMENTAL': 'Curiosity',\n",
    "    'threshold_NUANCE_EXPERIMENTAL': 'Nuance',\n",
    "    'threshold_PERSONAL_STORY_EXPERIMENTAL': 'Personal story',\n",
    "    'threshold_REASONING_EXPERIMENTAL': 'Reasoning',\n",
    "    'threshold_RESPECT_EXPERIMENTAL': 'Respect',\n",
    "    'any_positive_trait': 'Any positive trait',\n",
    "    'any_negative_trait': 'Any negative trait'\n",
    "}\n",
    "\n",
    "negative_threshold_attrs = [f'threshold_{col}' for col in negative_attrs if f'threshold_{col}' in df.columns]\n",
    "positive_threshold_attrs = [f'threshold_{col}' for col in positive_attrs if f'threshold_{col}' in df.columns]\n",
    "\n",
    "df['any_positive_trait'] = df[positive_threshold_attrs].any(axis=1).astype(int) if positive_threshold_attrs else 0\n",
    "df['any_negative_trait'] = df[negative_threshold_attrs].any(axis=1).astype(int) if negative_threshold_attrs else 0\n",
    "\n",
    "positive_for_chart = positive_threshold_attrs + ['any_positive_trait']\n",
    "negative_for_chart = negative_threshold_attrs + ['any_negative_trait']\n",
    "\n",
    "dem = df[df['Party'] == 'D']\n",
    "rep = df[df['Party'] == 'R']\n",
    "\n",
    "def pct_flagged(series):\n",
    "    return float(series.mean() * 100) if len(series) else 0.0\n",
    "\n",
    "def process_attributes(attrs):\n",
    "    rows = []\n",
    "    for col in attrs:\n",
    "        d_pct = pct_flagged(dem[col])\n",
    "        r_pct = pct_flagged(rep[col])\n",
    "        rows.append((attr_map[col], d_pct, r_pct, d_pct - r_pct, max(d_pct, r_pct)))\n",
    "    rows.sort(key=lambda x: x[4], reverse=True)\n",
    "    return [(label, d_pct, r_pct, diff) for label, d_pct, r_pct, diff, _ in rows]\n",
    "\n",
    "def create_dot_plot(ax, data, title):\n",
    "    if not data:\n",
    "        return\n",
    "    labels = [r[0] for r in data]\n",
    "    dem_pcts = np.array([r[1] for r in data])\n",
    "    rep_pcts = np.array([r[2] for r in data])\n",
    "    y = np.arange(len(labels))\n",
    "\n",
    "    for yi, xd, xr in zip(y, dem_pcts, rep_pcts):\n",
    "        ax.plot([xd, xr], [yi, yi], color='0.75', lw=1.5, zorder=1)\n",
    "\n",
    "    ax.scatter(dem_pcts, y, s=100, color=dem_color, edgecolor='black', linewidth=0.6, zorder=2, label='Democrats')\n",
    "    ax.scatter(rep_pcts, y, s=100, color=rep_color, edgecolor='black', linewidth=0.6, zorder=2, label='Republicans')\n",
    "    ax.set_yticks(y)\n",
    "    ax.set_yticklabels(labels, fontsize=20)\n",
    "    ax.set_xlabel('Percent of tweets flagged (%)', fontsize=20)\n",
    "    ax.tick_params(axis='x', labelsize=20)\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_xlim(0, 100)\n",
    "    ax.grid(axis='x', linestyle=\"--\", alpha=0.3)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.set_title(title, fontsize=20, pad=20)\n",
    "\n",
    "positive_data = process_attributes(positive_for_chart)\n",
    "negative_data = process_attributes(negative_for_chart)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10))\n",
    "create_dot_plot(ax1, positive_data, 'Positive Traits')\n",
    "ax1.legend(loc='upper left', frameon=False, fontsize=16)\n",
    "create_dot_plot(ax2, negative_data, 'Negative Traits')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=1.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a990027b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------\n",
    "#   FIGURE 3: Within-Member Fixed Effects of Tweet Attributes on Engagement\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "def calculate_content_type_fixed_effects_combined(df, content_type):\n",
    "    results = []\n",
    "    for author, author_df in df.groupby('authorUsername'):\n",
    "        if content_type == 'respectful':\n",
    "            content_tweets = author_df[author_df['threshold_RESPECT_EXPERIMENTAL'] == 1]\n",
    "        elif content_type == 'neutral':\n",
    "            content_tweets = author_df[\n",
    "                (author_df['threshold_TOXICITY'] == 0) &\n",
    "                (author_df['threshold_RESPECT_EXPERIMENTAL'] == 0)\n",
    "            ]\n",
    "        elif content_type == 'toxic':\n",
    "            content_tweets = author_df[author_df['threshold_TOXICITY'] == 1]\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "        if content_tweets.empty:\n",
    "            continue\n",
    "\n",
    "        results.append({\n",
    "            'author': author,\n",
    "            'party': author_df['Party'].iloc[0],\n",
    "            'total_tweets': len(author_df),\n",
    "            'content_tweets': len(content_tweets),\n",
    "            'content_rate': len(content_tweets) / len(author_df) * 100,\n",
    "            'likes_diff': content_tweets['likes'].mean() - author_df['likes'].mean(),\n",
    "            'reposts_diff': content_tweets['reposts'].mean() - author_df['reposts'].mean(),\n",
    "            'quotes_diff': content_tweets['quotes'].mean() - author_df['quotes'].mean()\n",
    "        })\n",
    "    return pd.DataFrame(results)\n",
    "\n",
    "def ci_95_mean(data):\n",
    "    clean = pd.Series(data).dropna()\n",
    "    return stats.sem(clean) * 1.96 if len(clean) > 1 else 0.0\n",
    "\n",
    "content_types = ['respectful', 'neutral', 'toxic']\n",
    "content_labels = ['Respectful', 'Neutral', 'Toxic']\n",
    "engagement_vars = ['likes', 'reposts', 'quotes']\n",
    "var_labels = ['Likes', 'Retweets', 'Quotes']\n",
    "content_colors = ['#2ca25f', 'lightgray', '#d73027']\n",
    "\n",
    "content_data = {}\n",
    "for content_type in content_types:\n",
    "    individual_effects = calculate_content_type_fixed_effects_combined(df, content_type)\n",
    "    content_data[content_type] = {\n",
    "        'averages': [individual_effects[f'{var}_diff'].mean() for var in engagement_vars] if not individual_effects.empty else [0, 0, 0],\n",
    "        'cis': [ci_95_mean(individual_effects[f'{var}_diff']) for var in engagement_vars] if not individual_effects.empty else [0, 0, 0],\n",
    "        'n_members': len(individual_effects)\n",
    "    }\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(18, 8))\n",
    "x = np.arange(len(var_labels))\n",
    "width = 0.25\n",
    "\n",
    "for i, (content_type, content_label, color) in enumerate(zip(content_types, content_labels, content_colors)):\n",
    "    ax.bar(\n",
    "        x + (i - 1) * width,\n",
    "        content_data[content_type]['averages'],\n",
    "        width,\n",
    "        label=content_label,\n",
    "        color=color,\n",
    "        alpha=0.7,\n",
    "        edgecolor='black',\n",
    "        yerr=content_data[content_type]['cis'],\n",
    "        capsize=5\n",
    "    )\n",
    "\n",
    "ax.axhline(0, color='black', linewidth=1)\n",
    "ax.set_ylabel(\"Fixed Effect vs Own Average\", fontsize=20)\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(var_labels, fontsize=20)\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.grid(True, linestyle=\"--\", alpha=0.3)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.legend(fontsize=20, frameon=False, loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07b9ef79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------\n",
    "#   FIGURE 4: Predicted Likes from Mixed-Effects Models of Toxicity and Respect\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "df_model = df.dropna(subset=[\"likes\", \"authorUsername\", \"Party\"]).copy()\n",
    "\n",
    "attributes = ['TOXICITY', 'RESPECT_EXPERIMENTAL']\n",
    "attribute_labels = ['Toxicity', 'Respect']\n",
    "for col in attributes:\n",
    "    df_model[f\"{col}_scaled\"] = df_model[col] * 100\n",
    "\n",
    "democrats_df = df_model[df_model[\"Party\"] == \"D\"]\n",
    "republicans_df = df_model[df_model[\"Party\"] == \"R\"]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 6), sharex=True)\n",
    "x_ticks = np.arange(0, 101, 20)\n",
    "y_ticks = np.arange(100, 401, 100)\n",
    "\n",
    "for i, (attr, label) in enumerate(zip(attributes, attribute_labels)):\n",
    "    formula = f\"log_likes ~ {attr}_scaled\"\n",
    "    ax = axes[i]\n",
    "\n",
    "    try:\n",
    "        dem_result = mixedlm(formula, democrats_df, groups=democrats_df[\"authorUsername\"]).fit()\n",
    "        rep_result = mixedlm(formula, republicans_df, groups=republicans_df[\"authorUsername\"]).fit()\n",
    "\n",
    "        col_range = np.linspace(0, 500, 200)\n",
    "\n",
    "        dem_predicted_likes = np.expm1(dem_result.params[\"Intercept\"] + dem_result.params[f\"{attr}_scaled\"] * col_range)\n",
    "        rep_predicted_likes = np.expm1(rep_result.params[\"Intercept\"] + rep_result.params[f\"{attr}_scaled\"] * col_range)\n",
    "\n",
    "        dem_coef_ci = dem_result.conf_int().loc[f\"{attr}_scaled\"]\n",
    "        rep_coef_ci = rep_result.conf_int().loc[f\"{attr}_scaled\"]\n",
    "\n",
    "        dem_ci_low = np.expm1(dem_result.params[\"Intercept\"] + dem_coef_ci[0] * col_range)\n",
    "        dem_ci_high = np.expm1(dem_result.params[\"Intercept\"] + dem_coef_ci[1] * col_range)\n",
    "        rep_ci_low = np.expm1(rep_result.params[\"Intercept\"] + rep_coef_ci[0] * col_range)\n",
    "        rep_ci_high = np.expm1(rep_result.params[\"Intercept\"] + rep_coef_ci[1] * col_range)\n",
    "\n",
    "        ax.plot(col_range, dem_predicted_likes, color=dem_color, linewidth=2, label='Democrats')\n",
    "        ax.fill_between(col_range, dem_ci_low, dem_ci_high, color=dem_color, alpha=0.2)\n",
    "        ax.plot(col_range, rep_predicted_likes, color=rep_color, linewidth=2, label='Republicans')\n",
    "        ax.fill_between(col_range, rep_ci_low, rep_ci_high, color=rep_color, alpha=0.2)\n",
    "\n",
    "        ax.set_title(label, fontsize=20)\n",
    "        ax.set_xlim(0, 100)\n",
    "        ax.set_ylim(0, 500)\n",
    "        ax.set_xticks(x_ticks)\n",
    "        ax.set_yticks(y_ticks)\n",
    "        ax.tick_params(axis='x', labelsize=16)\n",
    "        ax.tick_params(axis='y', labelsize=16)\n",
    "        ax.grid(True, linestyle=\"--\", alpha=0.3)\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "\n",
    "        if i == 0:\n",
    "            ax.set_ylabel(\"Predicted Likes\", fontsize=20)\n",
    "            ax.legend(fontsize=16, frameon=False, loc='upper left')\n",
    "\n",
    "    except Exception as e:\n",
    "        ax.text(\n",
    "            0.5, 0.5, f'Model failed for {label}\\n{str(e)}',\n",
    "            ha='center', va='center', transform=ax.transAxes, fontsize=16\n",
    "        )\n",
    "        ax.set_title(label, fontsize=20)\n",
    "        ax.set_xlim(0, 500)\n",
    "        ax.set_ylim(0, 500)\n",
    "        ax.set_xticks(x_ticks)\n",
    "        ax.set_yticks(y_ticks)\n",
    "        ax.set_xlabel(f\"{label} Score\", fontsize=18)\n",
    "        if i == 0:\n",
    "            ax.set_ylabel(\"Predicted Likes\", fontsize=20)\n",
    "        ax.tick_params(axis='x', labelsize=14)\n",
    "        ax.tick_params(axis='y', labelsize=14)\n",
    "        ax.grid(False)\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a79d4762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x450 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------\n",
    "#   FIGURE 5: Within-Member Fixed Effects of Tweet Attributes on Engagement by Party\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "def calculate_content_type_fixed_effects_by_party(df, content_type):\n",
    "    results = []\n",
    "    for author, author_df in df.groupby('authorUsername'):\n",
    "        party = author_df['Party'].dropna()\n",
    "        party = party.iloc[0] if not party.empty else None\n",
    "        if party not in ['D', 'R']:\n",
    "            continue\n",
    "\n",
    "        if content_type == 'respect':\n",
    "            content_tweets = author_df[author_df['threshold_RESPECT_EXPERIMENTAL'] == 1]\n",
    "        elif content_type == 'neutral':\n",
    "            content_tweets = author_df[\n",
    "                (author_df['threshold_TOXICITY'] == 0) &\n",
    "                (author_df['threshold_RESPECT_EXPERIMENTAL'] == 0)\n",
    "            ]\n",
    "        elif content_type == 'toxicity':\n",
    "            content_tweets = author_df[author_df['threshold_TOXICITY'] == 1]\n",
    "        else:\n",
    "            continue\n",
    "\n",
    "        if content_tweets.empty:\n",
    "            continue\n",
    "\n",
    "        results.append({\n",
    "            'author': author,\n",
    "            'party': party,\n",
    "            'likes_diff': content_tweets['likes'].mean() - author_df['likes'].mean(),\n",
    "            'reposts_diff': content_tweets['reposts'].mean() - author_df['reposts'].mean(),\n",
    "            'quotes_diff': content_tweets['quotes'].mean() - author_df['quotes'].mean(),\n",
    "            'total_tweets': len(author_df),\n",
    "            'content_tweets': len(content_tweets)\n",
    "        })\n",
    "    return pd.DataFrame(results)\n",
    "\n",
    "content_types = ['respect', 'neutral', 'toxicity']\n",
    "content_labels = ['Respect', 'Neutral', 'Toxicity']\n",
    "engagement_vars = ['likes', 'reposts', 'quotes']\n",
    "var_labels = ['Likes', 'Retweets', 'Quotes']\n",
    "\n",
    "party_content_data = {}\n",
    "for content_type in content_types:\n",
    "    individual_effects = calculate_content_type_fixed_effects_by_party(df, content_type)\n",
    "    party_content_data[content_type] = {}\n",
    "    for party in ['D', 'R']:\n",
    "        party_df = individual_effects[individual_effects['party'] == party]\n",
    "        party_content_data[content_type][party] = {\n",
    "            'averages': [party_df[f'{var}_diff'].mean() for var in engagement_vars] if not party_df.empty else [0, 0, 0],\n",
    "            'cis': [ci_95_mean(party_df[f'{var}_diff']) for var in engagement_vars] if not party_df.empty else [0, 0, 0],\n",
    "            'n_members': len(party_df)\n",
    "        }\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(14, 4.5), sharey=True)\n",
    "x = np.arange(len(var_labels))\n",
    "width = 0.36\n",
    "\n",
    "for i, (content_type, content_label) in enumerate(zip(content_types, content_labels)):\n",
    "    ax = axes[i]\n",
    "    dem_avgs = party_content_data[content_type]['D']['averages']\n",
    "    dem_cis = party_content_data[content_type]['D']['cis']\n",
    "    rep_avgs = party_content_data[content_type]['R']['averages']\n",
    "    rep_cis = party_content_data[content_type]['R']['cis']\n",
    "\n",
    "    ax.bar(\n",
    "        x - width / 2, dem_avgs, width, color=dem_color, alpha=0.7,\n",
    "        edgecolor='black', yerr=dem_cis, capsize=3, linewidth=0.6,\n",
    "        label='Democrats' if i == 0 else None\n",
    "    )\n",
    "    ax.bar(\n",
    "        x + width / 2, rep_avgs, width, color=rep_color, alpha=0.7,\n",
    "        edgecolor='black', yerr=rep_cis, capsize=3, linewidth=0.6,\n",
    "        label='Republicans' if i == 0 else None\n",
    "    )\n",
    "\n",
    "    ax.axhline(0, color='black', linewidth=1)\n",
    "    ax.set_title(content_label, fontsize=16)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(var_labels, fontsize=11)\n",
    "    ax.tick_params(axis='y', labelsize=10, labelleft=True)\n",
    "    ax.grid(True, linestyle=\"--\", alpha=0.3)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "\n",
    "axes[0].set_ylabel(\"Fixed Effect vs Own Average\", fontsize=13)\n",
    "axes[0].legend(frameon=False, fontsize=9, loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f33a61cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ------------------------------------------------------------------\n",
    "#   FIGURE 6: Effect of Linguistic Attributes on Likes for Toxic Tweets\n",
    "# ------------------------------------------------------------------\n",
    "\n",
    "df_clean = df.dropna(subset=[\"log_likes\", \"authorUsername\", \"Party\", \"threshold_TOXICITY\", \"threshold_RESPECT_EXPERIMENTAL\"])\n",
    "\n",
    "toxic_test_attributes = [\n",
    "    \"threshold_SEVERE_TOXICITY\", \"threshold_IDENTITY_ATTACK\", \"threshold_INSULT\",\n",
    "    \"threshold_PROFANITY\", \"threshold_THREAT\", \"threshold_SEXUALLY_EXPLICIT\",\n",
    "    \"threshold_AFFINITY_EXPERIMENTAL\", \"threshold_COMPASSION_EXPERIMENTAL\",\n",
    "    \"threshold_CURIOSITY_EXPERIMENTAL\", \"threshold_NUANCE_EXPERIMENTAL\",\n",
    "    \"threshold_PERSONAL_STORY_EXPERIMENTAL\", \"threshold_REASONING_EXPERIMENTAL\"\n",
    "]\n",
    "toxic_attribute_labels = [\n",
    "    \"Severe Toxicity\", \"Identity Attack\", \"Insult\", \"Profanity\",\n",
    "    \"Threat\", \"Sexually Explicit\", \"Affinity\", \"Compassion\",\n",
    "    \"Curiosity\", \"Nuance\", \"Personal Story\", \"Reasoning\"\n",
    "]\n",
    "\n",
    "respect_test_attributes = [\n",
    "    \"threshold_IDENTITY_ATTACK\", \"threshold_INSULT\",\n",
    "    \"threshold_PROFANITY\", \"threshold_THREAT\", \"threshold_SEXUALLY_EXPLICIT\",\n",
    "    \"threshold_AFFINITY_EXPERIMENTAL\", \"threshold_COMPASSION_EXPERIMENTAL\",\n",
    "    \"threshold_CURIOSITY_EXPERIMENTAL\", \"threshold_NUANCE_EXPERIMENTAL\",\n",
    "    \"threshold_PERSONAL_STORY_EXPERIMENTAL\", \"threshold_REASONING_EXPERIMENTAL\"\n",
    "]\n",
    "respect_attribute_labels = [\n",
    "    \"Identity Attack\", \"Insult\", \"Profanity\",\n",
    "    \"Threat\", \"Sexually Explicit\", \"Affinity\", \"Compassion\",\n",
    "    \"Curiosity\", \"Nuance\", \"Personal Story\", \"Reasoning\"\n",
    "]\n",
    "\n",
    "negative_plot_attrs = [\"Severe Toxicity\", \"Identity Attack\", \"Insult\", \"Profanity\", \"Threat\", \"Sexually Explicit\"]\n",
    "positive_plot_attrs = [\"Affinity\", \"Compassion\", \"Curiosity\", \"Nuance\", \"Personal Story\", \"Reasoning\"]\n",
    "\n",
    "toxic_tweets = df_clean[df_clean['threshold_TOXICITY'] == 1].copy()\n",
    "respectful_tweets = df_clean[df_clean['threshold_RESPECT_EXPERIMENTAL'] == 1].copy()\n",
    "\n",
    "def analyze_content_type_effects(base_tweets, test_attributes, attribute_labels, content_type_name):\n",
    "    comparison_results = []\n",
    "\n",
    "    for attr, label in zip(test_attributes, attribute_labels):\n",
    "        with_attr = int((base_tweets[attr] == 1).sum())\n",
    "        without_attr = int((base_tweets[attr] == 0).sum())\n",
    "        attr_rate = with_attr / len(base_tweets) * 100 if len(base_tweets) else 0\n",
    "\n",
    "\n",
    "\n",
    "        if with_attr < 25:\n",
    "            comparison_results.append({\n",
    "                'attribute': label,\n",
    "                'n_with_attr': with_attr,\n",
    "                'n_without_attr': without_attr,\n",
    "                'attr_rate': attr_rate,\n",
    "                'pct_change': np.nan,\n",
    "                'pct_change_ci': np.nan,\n",
    "                'p_value': np.nan,\n",
    "                'significant': False,\n",
    "                'direction': 'insufficient_data'\n",
    "            })\n",
    "            continue\n",
    "\n",
    "        try:\n",
    "            result = mixedlm(f\"log_likes ~ {attr}\", base_tweets, groups=base_tweets[\"authorUsername\"]).fit()\n",
    "\n",
    "            intercept = result.params.get('Intercept', 0)\n",
    "            attr_coef = result.params.get(attr, 0)\n",
    "            attr_p = result.pvalues.get(attr, 1)\n",
    "            attr_ci = result.conf_int().loc[attr] if attr in result.conf_int().index else [0, 0]\n",
    "\n",
    "            likes_without = np.exp(intercept)\n",
    "            likes_with = np.exp(intercept + attr_coef)\n",
    "            pct_change = attr_coef * 100 if attr_coef != 0 else 0\n",
    "            ci_low_coef = attr_ci[0] * 100\n",
    "            ci_high_coef = attr_ci[1] * 100\n",
    "            pct_change_ci = max(abs(pct_change - ci_low_coef), abs(ci_high_coef - pct_change))\n",
    "\n",
    "\n",
    "\n",
    "            if attr_p < 0.10:\n",
    "                direction = \"increases\" if attr_coef > 0 else \"decreases\"\n",
    "\n",
    "            comparison_results.append({\n",
    "                'attribute': label,\n",
    "                'n_with_attr': with_attr,\n",
    "                'n_without_attr': without_attr,\n",
    "                'attr_rate': attr_rate,\n",
    "                'pct_change': pct_change,\n",
    "                'pct_change_ci': pct_change_ci,\n",
    "                'coefficient': attr_coef,\n",
    "                'p_value': attr_p,\n",
    "                'significant': attr_p < 0.10,\n",
    "                'direction': 'increase' if attr_coef > 0 else 'decrease'\n",
    "            })\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"  Model failed: {str(e)}\")\n",
    "            comparison_results.append({\n",
    "                'attribute': label,\n",
    "                'n_with_attr': with_attr,\n",
    "                'n_without_attr': without_attr,\n",
    "                'attr_rate': attr_rate,\n",
    "                'pct_change': np.nan,\n",
    "                'pct_change_ci': np.nan,\n",
    "                'p_value': np.nan,\n",
    "                'significant': False,\n",
    "                'direction': 'model_failed'\n",
    "            })\n",
    "\n",
    "    return pd.DataFrame(comparison_results)\n",
    "\n",
    "toxic_results = analyze_content_type_effects(toxic_tweets, toxic_test_attributes, toxic_attribute_labels, \"Toxic\")\n",
    "respect_results = analyze_content_type_effects(respectful_tweets, respect_test_attributes, respect_attribute_labels, \"Respectful\")\n",
    "\n",
    "toxic_valid = toxic_results.dropna(subset=['pct_change'])\n",
    "respect_valid = respect_results.dropna(subset=['pct_change'])\n",
    "\n",
    "all_values = []\n",
    "for valid_df in [toxic_valid, respect_valid]:\n",
    "    if not valid_df.empty:\n",
    "        all_values.extend((valid_df['pct_change'] + valid_df['pct_change_ci']).tolist())\n",
    "        all_values.extend((valid_df['pct_change'] - valid_df['pct_change_ci']).tolist())\n",
    "\n",
    "if all_values:\n",
    "    ymin, ymax = min(all_values), max(all_values)\n",
    "    y_range = ymax - ymin\n",
    "    padding = y_range * 0.1 if y_range > 0 else 5\n",
    "    global_ylims = (ymin - padding, ymax + padding)\n",
    "else:\n",
    "    global_ylims = (-10, 10)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 12))\n",
    "\n",
    "def create_effect_plot(ax, results_df, title):\n",
    "    if results_df.empty:\n",
    "        ax.text(0.5, 0.5, 'No valid data', ha='center', va='center', transform=ax.transAxes)\n",
    "        ax.set_title(title, fontsize=16, fontweight='bold')\n",
    "        return\n",
    "\n",
    "    attributes = results_df['attribute']\n",
    "    pct_changes = results_df['pct_change']\n",
    "    pct_change_cis = results_df['pct_change_ci']\n",
    "    x = np.arange(len(attributes))\n",
    "    colors = ['#d73027' if attr in negative_plot_attrs else '#2ca25f' for attr in attributes]\n",
    "\n",
    "    ax.bar(\n",
    "        x, pct_changes, yerr=pct_change_cis, capsize=5,\n",
    "        color=colors, alpha=0.7, edgecolor='black', linewidth=1\n",
    "    )\n",
    "    ax.axhline(0, color='black', linewidth=1)\n",
    "    ax.set_ylabel('Percentage Change in Likes (%)', fontsize=14)\n",
    "    ax.set_title(title, fontsize=20)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(attributes, rotation=45, ha='right', fontsize=16)\n",
    "    ax.tick_params(axis='y', labelsize=16)\n",
    "    ax.grid(True, linestyle=\"--\", alpha=0.3)\n",
    "    ax.set_ylim(global_ylims)\n",
    "\n",
    "create_effect_plot(ax1, toxic_valid, 'Effect of Additional Attributes on Toxic Tweet Engagement')\n",
    "create_effect_plot(ax2, respect_valid, 'Effect of Additional Attributes on Respectful Tweet Engagement')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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